# Neural Reconstruction Integrity: A metric for assessing the connectivity of reconstructed neural networks

## TL;DR

This paper introduces a new metric called Neural Reconstruction Integrity for evaluating the accuracy of reconstructed neural networks, focusing on neuron connectivity rather than voxel-level detail, to improve assessment of brain graph reconstruction methods.

## Contribution

The paper presents a novel, neuron-centric metric for assessing neural network reconstructions that is robust to segmentation errors and more aligned with biological connectivity accuracy.

## Key findings

- The metric effectively measures neuron integrity in reconstructed networks.
- It is insensitive to small segmentation errors.
- Demonstrated on simulated neural network data.

## Abstract

Neuroscientists are actively pursuing high-precision maps, or graphs, consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we develop a metric for measuring the performance of such methods by comparing their output with those generated by human annotators ("ground truth" data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the "integrity" of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors.